The present invention relates to target detection, and more particularly to target detection using an automatic threshold system in infrared (IR) and radar detectors to differentiate between targets and clutter in high noise environments.
One application for large imaging arrays is in scanning sensors for detecting and locating the onset of a radiative event in a cluttered or noisy environment. For example, a satellite-based sensor array can be used to scan a region on earth to detect missile or spacecraft launchings or nuclear tests. The sensors detect radiated energy in for example the infrared spectrum and electrical signals are generated by the scanning sensors. Electrical signals of sufficient amplitude would represent targets, or bright spots of radiative energy.
A key problem in infrared and radar systems is the need to detect the electrical signals caused by legitimate radiating targets and reject those caused by noise or background clutter. This is differentiation process is classically accomplished by "thresholding". A predetermined threshold is compared to the received signal. If the amplitude of the electrical signal is greater than the threshold then a target has been detected.
Known methods and systems for computing signal thresholds typically partition the geographic area scanned into a number of "threshold zones". A different threshold value is then computed for each zone. Every point within a given zone gets the same threshold value. This method works well when the noise statistics are approximately constant over the entire zone. However, this threshold zone method does not provide reliable data when radiative clutter is present.
Clutter occurs when portions of the zone scanned are very "noisy" or bright while other areas are very "quiet" or dark. Noisy areas occur when the sensor receives non-target generated radiation from the earth's atmosphere or from natural radiating elements on the earth's surface such as bodies of water. Additionally, man-made radiation from manufacturing processes or weapon detonations would also produce false targets as viewed by a scanning sensor.
The computed threshold value is driven up by the noisiest portion of the zone. This results in very poor probability of target detection for targets near clutter areas. The method of computing threshold values is typically to count the number of threshold exceedances (false detections) for each zone on one or more frames of data and then compute the threshold value needed to achieve the desired false detection rate. The computed threshold value is not applied until the next frame so that there is a lag in response to changing noise or clutter conditions.
When counting threshold exceedances, present thresholding methods cannot distinguish between exceedances caused by noise, and those exceedances which may be caused by true targets. The sudden appearance of a large number of targets may cause thresholds to be raised resulting in poor target detection probability. The technique commonly used to prevent this event is to further delay application of the computed threshold until some outside intelligence such as a human operator, determines whether the signal threshold should be raised or not.
Disadvantages of the known zone thresholding systems and method include: poor overall performance near clutter; poor performance in rapidly changing noise conditions (for example during battle field counter-measure) and required human intervention to set or adjust thresholds.